The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
V. V. Dovgalyuk - One of the best experts on this subject based on the ideXlab platform.
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Sunspot cycle timing: A secular forecast
Astronomical & Astrophysical Transactions, 1996Co-Authors: M. V. Fyodorov, V. V. Klimenko, V. V. Dovgalyuk, S. Yu. SnytinAbstract:Abstract The spectral analysis of sunspot minima dates time series demonstrates that a high degree of determinism is characteristic of these data. A Simple Regression Model involving a linear trend (11.083 yrs/cycle) and 3 harmonic functions (with periods corresponding to 18.6, 8.8 and 7 Schwabe cycles) allows us to develop an extra-long sunspot cycle timing forecast.
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Sunspot minima dates: A secular forecast
Solar Physics, 1996Co-Authors: M. V. Fyodorov, V. V. Klimenko, V. V. DovgalyukAbstract:The spectral analysis of sunspot minima date time series demonstrates that a high degree of determinism is peculiar to these data. A Simple Regression Model involving a linear trend (11.083 yr cycle-1) and 3 harmonic functions (with periods corresponding to 18.6, 8.8, and 7.0 Schwabe cycles) allows development of an extra-long sunspot-cycle timing forecast.
M. V. Fyodorov - One of the best experts on this subject based on the ideXlab platform.
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Sunspot cycle timing: A secular forecast
Astronomical & Astrophysical Transactions, 1996Co-Authors: M. V. Fyodorov, V. V. Klimenko, V. V. Dovgalyuk, S. Yu. SnytinAbstract:Abstract The spectral analysis of sunspot minima dates time series demonstrates that a high degree of determinism is characteristic of these data. A Simple Regression Model involving a linear trend (11.083 yrs/cycle) and 3 harmonic functions (with periods corresponding to 18.6, 8.8 and 7 Schwabe cycles) allows us to develop an extra-long sunspot cycle timing forecast.
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Sunspot minima dates: A secular forecast
Solar Physics, 1996Co-Authors: M. V. Fyodorov, V. V. Klimenko, V. V. DovgalyukAbstract:The spectral analysis of sunspot minima date time series demonstrates that a high degree of determinism is peculiar to these data. A Simple Regression Model involving a linear trend (11.083 yr cycle-1) and 3 harmonic functions (with periods corresponding to 18.6, 8.8, and 7.0 Schwabe cycles) allows development of an extra-long sunspot-cycle timing forecast.
Ch. E. Minder - One of the best experts on this subject based on the ideXlab platform.
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A Simple Regression Model for network meta-analysis
OA Epidemiology, 2013Co-Authors: Alphons G.h. Kessels, G. Ter Riet, Milo A. Puhan, J. Kleijnen, L. M. Bachmann, Ch. E. MinderAbstract:Introduction: The aim of this paper is to propose a transparent, alternative approach for network meta-analysis based on a Regression Model that allows inclusion of studies with three or more treatment arms. Methodology: Based on the contingency tables describing the frequency distribution of the outcome in the different intervention arms, a data set is constructed. A logistic Regression is used to determine the parameters describing the difference in effect between a specific intervention and the reference intervention and to check the assumptions needed to Model the effect parameters. The method is demonstrated by re-analysing 24 studies investigating the effect of smoking cessation interventions. The results of the analysis were similar to two other published approaches to network analysis using the same data set. The presence of heterogeneity, including inconsistency, was examined. Conclusion: The proposed method provides an easy and transparent way to estimate treatment effect parameters in metaanalyses involving studies with more than two arms. It has several additional attractive features such as not overweighting small studies as the random effect Models do, dealing with zero count cells, checking of assumptions about the distribution of Model parameters and investigation of heterogeneity across trials and between direct and indirect evidence.
Katia Radja - One of the best experts on this subject based on the ideXlab platform.
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An Analysis of Social Proximity and Interest Rate in Rural South India
Economics & Sociology, 2018Co-Authors: Augendra Bhukuth, Jérôme Ballet, Katia RadjaAbstract:In this article we present the role of social proximity in the formation of the interest rate in the informal credit market in the state of Tamil Nadu, India. This paper is unique in the sense that it deals with social proximity and the formation of interest rate. If a lot of articles deal with the interest rate and social capital, none of them have attempted to link the two variables. A qualitative and quantitative survey was conducted on debt bondage in 2003-2004. The impact of social proximity defined as strong and weak is captured by a Simple Regression Model. We show that a strong social proximity has a negative impact on the interest rate. Furthermore, the frequency of transactions has a positive impact on the interest rate, which means that asymmetrical information does not play a central role in our study. We then explain the relation between social proximity and interest rate by using the concept of rights and obligations.
Juko Ando - One of the best experts on this subject based on the ideXlab platform.
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Direction of causation between shared and non-shared environmental factors.
Behavior genetics, 2009Co-Authors: Koken Ozaki, Juko AndoAbstract:Determining the direction of causation between two related variables is an interesting and challenging problem. A Simple Regression Model is a frequently used statistical tool to find out whether a dependent variable is significantly predicted by an independent variable; however using a Simple Regression Model cannot determine the direction of causation, because the Model fit takes no account of this direction. As an approach to this problem, non-normal structural equation Modeling (nnSEM; Shimizu and Kano, J Stat Plan Inference 138:3483–3491, 2008) using higher order moments (third, fourth,…) as well as first and second order moments, can be useful. This method enables us to determine the direction of causation using goodness of fit, even for a Simple Regression Model. In this paper, nnSEM is applied to behavior genetics, in particular, to the genetic Simplex Model. In this context, nnSEM enables us to determine the direction of causation between C (shared environment) factors and between E (non-shared environment) factors. The efficiency of this method is illustrated by simulation studies and the analysis of real longitudinal twin data.